Evidence map›Paper›PMID 39599401›Full record

ArticlePlants (Basel, Switzerland)2024

An Efficient Weed Detection Method Using Latent Diffusion Transformer for Enhanced Agricultural Image Analysis and Mobile Deployment.

Yuzhuo Cui, Yingqiu Yang, Yuqing Xia, Yan Li, Zhaoxi Feng, Shiya Liu, Guangqi Yuan, Chunli Lv

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plants (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yuzhuo CuiCollege of Electrical and Information Engineering, China Agricultural University, Beijing 100083, China.
Yingqiu YangCollege of Electrical and Information Engineering, China Agricultural University, Beijing 100083, China.
Yuqing XiaCollege of Electrical and Information Engineering, China Agricultural University, Beijing 100083, China.
Yan LiCollege of Electrical and Information Engineering, China Agricultural University, Beijing 100083, China.
Zhaoxi FengCollege of Electrical and Information Engineering, China Agricultural University, Beijing 100083, China.
Shiya LiuCollege of Electrical and Information Engineering, China Agricultural University, Beijing 100083, China.
Guangqi YuanCollege of Electrical and Information Engineering, China Agricultural University, Beijing 100083, China.
Chunli LvCollege of Electrical and Information Engineering, China Agricultural University, Beijing 100083, China.

Funding

National Natural Science Foundation of China 61202479
6 · The paper itself

Abstract

This paper presents an efficient weed detection method based on the latent diffusion transformer, aimed at enhancing the accuracy and applicability of agricultural image analysis. The experimental results demonstrate that the proposed model achieves a precision of 0.92, a recall of 0.89, an accuracy of 0.91, a mean average precision (mAP) of 0.91, and an F1 score of 0.90, indicating its outstanding performance in complex scenarios. Additionally, ablation experiments reveal that the latent-space-based diffusion subnetwork outperforms traditional models, such as the the residual diffusion network, which has a precision of only 0.75. By combining latent space feature extraction with self-attention mechanisms, the constructed lightweight model can respond quickly on mobile devices, showcasing the significant potential of deep learning technologies in agricultural applications. Future research will focus on data diversity and model interpretability to further enhance the model's adaptability and user trust.

Indexed as

agricultural image analysisdeep learninglatent diffusion transformerreal-time monitoringweed detection

Identifiers

PMID39599401
PMCPMC11598004

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.